compare-mt: A Tool for Holistic Comparison of Language Generation Systems

Graham Neubig, Zi-Yi Dou, Junjie Hu, Paul Michel, Danish Pruthi, Xinyi Wang


Abstract
In this paper, we describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation. The main goal of the tool is to give the user a high-level and coherent view of the salient differences between systems that can then be used to guide further analysis or system improvement. It implements a number of tools to do so, such as analysis of accuracy of generation of particular types of words, bucketed histograms of sentence accuracies or counts based on salient characteristics, and extraction of characteristic n-grams for each system. It also has a number of advanced features such as use of linguistic labels, source side data, or comparison of log likelihoods for probabilistic models, and also aims to be easily extensible by users to new types of analysis. compare-mt is a pure-Python open source package, that has already proven useful to generate analyses that have been used in our published papers. Demo Video: https://youtu.be/NyJEQT7t2CA
Anthology ID:
N19-4007
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
35–41
Language:
URL:
https://aclanthology.org/N19-4007
DOI:
10.18653/v1/N19-4007
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/N19-4007.pdf
Supplementary:
 N19-4007.Supplementary.pdf